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SASInstitute A00-406 Exam Syllabus Topics:

SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:

1. In a machine learning pipeline, what is the purpose of cross-validation?

A) To visualize the data distribution
B) To split the dataset into training and testing sets
C) To train multiple models on different subsets of the data to assess generalization
D) To evaluate the model's performance on new data

2. In natural language processing, what does "stemming" involve?

A) Reducing words to their base or root form
B) Creating new words to improve model performance
C) Grouping similar words together based on their meanings
D) Converting text to numbers for model input

3. Which type of model is well-suited for solving classification problems when dealing with high- dimensional data, such as text?

A) K-Means Clustering
B) Support Vector Machine (SVM)
C) Linear Regression
D) Random Forest

4. Which hyperparameter in a decision tree model controls the depth of the tree and helps prevent overfitting?

A) Min samples split
B) Max features
C) Max depth
D) Learning rate

5. What is "model reevaluation" in the model deployment phase?

A) The evaluation of data distribution
B) The periodic assessment of a deployed model's performance and potential retraining
C) The process of data preprocessing
D) The process of selecting features

Solutions: